Understanding Multi-Channel Attribution Modelling

As a business, do you have problems when it comes to identifying and measuring user actions on your website in a valuable way? Do you struggle to monitor several channels and report on how your marketing budget is being spent, and how you are optimising revenue?

You are not alone. In fact, many businesses seeking expert digital marketing advice have these very issues.

Understanding attribution and improving the way you measure the success of your digital marketing efforts can be the key to unlocking data that will help you realise what really does drive the best ROI.

This article is going to explain attribution modelling in Google Analytics and what you need to do in order to get powerful results now and in the future.

What is marketing attribution anyway?

It’s a simple concept on the face of it. Here’s a quote from Wikipedia, which sums it up pretty well:

“Marketing attribution provides a level of understanding of what combination of events in what particular order influence individuals to engage in a desired behavior, typically referred to as a conversion.”

So in a digital marketing sense, attribution is how you identify the value in the actions people take on your website that lead to a conversion. A conversion could be an ecommerce sale, or it could be a visitor giving you their contact details. But if this conversion happened as a result of the visitor finding your website through three different channels, are you attributing the appropriate percentage of that conversion to each channel? If the answer is “No” or “I’m not sure”, keep reading.

Digital marketing channels

When we’re thinking about multi-channel attribution in relation to your website, we’re thinking about channels such as:

Organic search traffic

Paid search traffic

Referral traffic

Social traffic

Direct traffic

Email traffic

When you delve into attribution, you can find out what the most common conversion path structures look like, and thus what journey your visitors are taking before they make that purchase, or give you their contact details. This helps you to improve your strategy and find ways to optimise conversions as well as identify how to distribute your budget.

Note: I have to address the elephant in the room – we are focusing on digital marketing channels here. You may want to consider attribution across offline channels too. But that’s a whole challenge in it’s own right which won’t be covered right now.

Attribution models

Let’s take a look at the standard out-of-the-box attribution models that Google Analytics has to offer. I will also include the guidelines that Google provides on when each model is useful.

Last Interaction

This model attributes 100% of the conversion value to the last channel the visitor interacted with. It could be useful if your ads and campaigns are designed to attract visitors at the point of conversion, or if you have a short sales funnel with no consideration period.

Last Non-Direct Click

This model attributes 100% of the conversion value to the last channel clicked through from, ignoring direct sessions (traffic). It could be used if you consider customers from direct sessions to have already been ‘won’ by another channel.

This is the standard model used in all non-multi-channel reports you see in Google Analytics(!)

Last AdWords Click

This model attributes 100% of the conversion value to the most recent AdWords ad that was clicked prior to conversion. It is intended for us if you want to find out which of your ads close the most conversions.

First Interaction

This model attributes 100% of the conversion value to the first channel the customer interacted with. It could be used if you are aiming to raise awareness with your campaigns and ads, and place more value in first interactions.

Do you spot any issues yet?

If you are thinking, “hang on I can’t just attribute 100% of the value of a conversion to one channel, that’s absurd”, then you get extra points. It gets better though, the following models show something a little more promising…

Linear

This model gives equal attribution to each interaction on the path to conversion. It could be useful if your aim is to maintain contact and awareness through every stage of the buying cycle. (But still use with caution!)

Time Decay

This model gives more credit to the touchpoints closest to the conversion in terms of time, and less to those further away. This model is useful if you value those actions that are closer to the conversion, or if you have a shorter buying cycle.

Time Decay is more useful as a standard option, but if you have a particularly long buying cycle, or are worried about channels such as social getting very little attribution, then it could cause trouble.

However, you are able to tweak this base model, and adjust the half-life of decay, lookback window and adjust credit based on engagement:

Position Based

This model allows you to split the attribution by varying amounts. By default, GA will assign 40% to the first and last interaction and 20% to interactions in the middle. This model is useful if you most value the first and last interactions, but still want to give attribution to the touchpoints in-between.

Again, you can create a custom copy of the Position Based model, but it requires some dedicated thought, research and ongoing testing to get this right:

Custom Models

It is safe to say that these out-of-the-box models could cause more problems than they solve, and that you should seek to build and test a custom attribution model based on data and your specific business needs. For example, by taking into consideration the type of behaviour you value the most, historical data about your sales funnel and conversion window, typical behaviour and factors such as repeat conversions.

If you are going to start testing custom models, a good place to start would be to tweak the Time Decay and Position Based models to see how it affects your data and learn from this process.

Finding the right attribution model

You’re trying to find a model that works for you. Often, conversion credit or attribution is given to actions at the bottom of the funnel, like PPC or landing page conversions. If this is the case, you may be neglecting to give appropriate credit to actions at the top of the funnel, which can mean areas like social and content marketing are undervalued.

Without dedicating time and resource to understanding and getting the most out of your Analytics data you could be missing out on many valuable insights. You may be attributing 100% of the credit to a conversion from a visitor who came direct, despite the fact they clicked two ads and also found you through organic search results prior to conversion.

Conversely, and more likely, you may be completely undervaluing direct traffic under the standard Last Non-Direct Click model, which can take the glory away from your efforts in building brand awareness and improving loyalty and retention.

Multi-channel reports in Analytics

Let’s take a look at some of the data you can look at right now to get you started.

If you head over to the ‘Multi-Channel Funnels’ section in Google Analytics, and click overview:

This shows you a visual of where your channels overlap. That means where the path to conversion contains two or more channels. So this gives a simple overview of where attribution can help you better understand your performance data. Pretty cool!

Next, head to the model comparison tool:

This tool allows you to select and compare different models. In the example below, Last Non-Direct Click is being compared with Time Decay. You can see that using Last Non-Direct Click, attribution for the direct channel has been distributed to other channels, compared to Time Decay where the direct channel has an increase of 75% by comparison.

This is very good at highlighting your digital multi-channel attribution in just a few clicks. The only hitch is that the information provided is only as good as the model you use, and as we have discovered, the GA standard models aren’t necessarily going to be providing the right model for you.

Here’s some key things to take away that you can action right away to improve your attribution:

Ensure you have successfully set up conversion tracking, including micro and macro conversions.

If you haven’t already, do some analysis to figure out your conversion values in economic terms where possible, and add these values to Google Analytics.

The same goes for adding cost data for CPA (cost per acquisition) to GA for non-paid channels such as organic, referral, social and email. In the example above, only paid search is showing CPA, but ideally you want this data for all of your channels.

Make sure you are keeping on top of costs associated with content and content marketing so that you can report on ROI.

Make sure you track all of your campaigns using parameters. This one is very important if you want multi-channel attribution to work.

Marketing and sales

As a relevant aside, it’s worth mentioning that attribution is a prime example of where your marketing and sales teams need to get their heads together.

Here’s some over-arching metrics that you need to get a handle of that will give a benchmark to provide better insights on performance and ROI for digital campaigns:

Cost per lead / cost per acquisition — How much does the company have to spend to get a lead?

Lead-to-customer percentage — How many leads do you turn into customers?

Average order value — How much do your customers spend on average in a transaction? Especially useful if you have an ecommerce website.

Lifetime order value / average revenue per customer — How much do customers spend in total. Particularly useful for B2B companies who have a long sales funnel and do not have an ecommerce website.

Customer retention — Is your total number of customers increasing or falling? This can give you some early warning signs and influence your strategy.

It is also worth mentioning that all digital teams and wider teams need to agree on the goals of your attribution program and the models you are testing. As with all things marketing, you need to be continually testing and redefining what is right for you.

Digital teams that are working in channel based silos (PPC, SEO, Content) may all have different ideas on how they think conversion value should be attributed, so it’s important to ensure you work on a data driven approach that everyone can fully get behind and benefit from.

Conclusion

For most, your biggest marketing aims will be to turn your leads into customers, keep your cost per lead at a level that makes business sense and to retain your customers for longer. In order to get powerful data to report on your digital marketing success and ROI in a multi-channel world, you need to become familiar with attribution modelling.

If you are using Google Analytics, I would recommend starting with the Time Decay model to begin with and then start to customise your attribution approach, with the support of your knowledge, research and business data.

Need help getting the most out of Analytics? If your business needs expert advice on turning Analytics data into valuable insight, contact us by email, or call 0800 622 6100.